Yanqiu Feng1,2,3, Mengye Lyn1,2, Yilong Liu1,2, Victor X Bin1,2, and Ed X Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China, People's Republic of, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China, People's Republic of, 3School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China, People's Republic of
Synopsis
This work develops a novel super
slice interpolation (SSI) method that
exploits sensitivity variation along the slice direction to decrease slice
thickness in multi-slice MRI. The phantom and in vivo
head imaging results demonstrate that SSI
successfully generates thinner slice images
from a set of thick images acquired with multiple receiver coils, without the need of modifying pulse
sequences. The proposed SSI method has potential to obtain more slices with
decreased thickness in scenarios where acquisition window or specific
absorption rate limits the available number of slices.Purpose
Standard parallel MRI techniques accelerate
imaging acquisition by exploiting sensitivity variation in array coils to unfold
aliased images [1] or replace phase
encoding steps [2] in the
reconstruction of equi-spacedly subsampled MRI data. In multi-slice MRI, the
number of available slices is usually limited by scan time, data acquisition
window or specific absorption rate (SAR). The goal of this work is to develop a
super slice interpolation (SSI) method for multi-slice MRI by exploiting sensitivity
variation along the slice direction to reconstruct thinner slice images from a
set of thick images acquired with multiple receiver coils.
Methods
Figure 1 illustrates the concept of
proposed SSI method. SSI generates multiple thinner slices from each acquired
thick slice using coil sensitivity maps acquired with thinner slices. Let dl(i,j) denote a pixel in a
thick slice of the l-th coil at
location (i,j), the encoding equation of parallel multi-slice MRI can be
formulated as: $$$d_{l}\left(i,j\right)=\sum_k^Ks_{l}\left(i,j,k\right)m\left(i,j,k\right),l=1,2,...,L$$$ (1)
where m(i,j,k),
k = 1, 2, …, K, denotes pixels in thin slices which
contribute to signal dl(i,j) and K is the number of the target pixels, sl(i,j,k) denotes sensitivity
of the l-th coil at location (i,j,k)
and L is the number of coils. For
brevity, the above encoding equation can be expressed in a vector form:
$$$d=Em$$$ (2)
where E denotes the sensitivity encoding matrix constructed form
sensitivity maps. The goal of SSI is to reconstruct m by inversing Eq. (2).
Because lower
intra-voxel sensitivity variation along the slice direction leads to severely
ill-conditioned encoding matrix, the direct inversion of Eq. (2) will result in
large noise amplification. To suppress noise amplification, Tikhonov
regularization is introduced to reconstruction[3],
which can be formulated as:
$$$\widehat{m}=argmin_m\left\{\mid\mid Em-d\mid\mid_2^2+\lambda\mid\mid m\mid\mid_2^2\right\}$$$ (3)
where $$$\mid\mid \Box \mid\mid_2^2$$$ denote the square of L2 norm, and λ is the regularization parameter which
controls the noise suppression effect but at the expense of spatial resolution
along the slice direction. In this work, λ
was experientially determined as 0.2 by a visual inspection for the tradeoff
between signal-to-noise ratio (SNR) and slice separation accuracy.
The performance of
proposed SSI method was evaluated on multi-slice fast spin echo data acquired
on a Philips Achieva 3T MRI scanner. Phantom data were acquired with a sixteen-channel
abdomen coil, and in vivo head data were acquired with an eight-channel human
head coil. The scan parameters were: TE/TR = 80/3000 ms, FOV = 230×230 mm2,
matrix size = 230×230, slice gap = 0 mm, echo train length = 16. Thirty-two slices with 6 mm thickness were acquired as thick slices, and
sixty-four slices with 3 mm thickness were acquired as thin slices for
performance evaluation. Sensitivity maps were calculated as the multi-coil thin
slice images divided by their root-sum-of-square combination.
Results
Figure 2 and 3 separately show phantom imaging
results from two typical acquired 6 mm slice image. From regions pointed by
solid arrows, it can be observed that the SSI-generated 3 mm slice images contained
structures close to those in the directly acquired 3 mm slice images. Figure 4 shows
in vivo head imaging results from one typical 6 mm slice image. The structures
and margins were blurred in the acquired 6 mm thickness images. In contrast,
the SSI-separated images exhibited more clearly defined margins, as pointed by
arrows, which were similar to those in directly acquired 3 mm thickness images.
It can also be observed that the cerebellum in the SSI results were
successfully separated into two parts, which were similar to corresponding
contents in directly acquired 3 mm thickness images.
Discussion
The proposed SSI
method provides a novel way to exploit sensitivity variation along the slice
direction to decrease slice thickness in multi-slice MRI. SSI is a totally postprocessing
method with coil sensitivity, and thus applicable to all multi-slice
acquisitions without the need to modify pulse sequences. SSI may help in
tackling the high SAR problem of fast spin echo imaging particularly at high
field MRI, and increasing the slice resolution of multi-slice echo planer
imaging in dynamic MRI.
Conclusion
Our
results clearly demonstrated the capability of SSI in generating thin slice
images from thick slice MRI data. The proposed SSI method can be combined with
in-plane parallel MRI methods for further acceleration, and has potential to
obtain more slices with decreased thickness in scenarios where acquisition
window or SAR limits the available number of slices.
Acknowledgements
No acknowledgement found.References
[1] PruessmannK. P., Magn
Reson Med1999;42(5):952-962.
[2] Griswold, M. A., Magn
Reson Med 2002;47(6):1202-10.
[3] Otazo, R., NeuroImage
2009;47:220-230.